The inpainting game provides a quantitative comparison of XFR algorithms, however it does not provide insight as to how useful these XFR algorithms are on novel face images. In this section, we provide a qualitative study of XFR algorithms visualized on a standard set of triplets.
We consider two target networks: ResNet-101 and Light-CNN [1]}, and provide visualizations for the whitebox methods referenced in
the main submission.
This analysis includes the following figures showing qualitative visualization results for combinations of (target network, XFR method): (ResNet-101, EBP, Fig. REF ), (ResNet-101, cEBP, Fig. REF ), (ResNet-101, tcEBP, Fig. REF ), (ResNet-101, Subtree, Fig. REF ), (Light-CNN, EBP, Fig. REF ), (Light-CNN, cEBP, Fig. REF ), (Light-CNN, tcEBP, Fig. REF ), (Light-CNN, Subtree EBP, Fig. REF ). Finally, we show results for the Light-CNN using only single probes (Fig. REF ), or repeated probes (Fig. (REF ) to highlight the effect of non-mates in the triplet visualization.
